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Vehicle-to-Everything (V2X) communication enables vehicles to communicate with other vehicles and roadside infrastructure, enhancing traffic management and improving road safety. However, the open and decentralized nature of V2X networks exposes them to various security threats, especially misbehaviors, necessitating a robust Misbehavior Detection System (MBDS). While Machine Learning (ML) has proved effective in different anomaly detection applications, the existing ML-based MBDSs have shown limitations in generalizing due to the dynamic nature of V2X and insufficient and imbalanced training data. Moreover, they are known to be vulnerable to adversarial ML attacks. On the other hand, Generative Adversarial Networks (GAN) possess the potential to mitigate the aforementioned issues and improve detection performance by synthesizing unseen samples of minority classes and utilizing them during their model training. Therefore, we propose the first application of GAN to design an MBDS that detects any misbehavior and ensures robustness against adversarial perturbation. In this article, we present several key contributions. First, we propose an advanced threat model for stealthy V2X misbehavior where the attacker can transmit malicious data and mask it using adversarial attacks to avoid detection by ML-based MBDS. We formulate two categories of adversarial attacks against the anomaly-based MBDS. Later, in the pursuit of a generalized and robust GAN-based MBDS, we train and evaluate a diverse set of Wasserstein GAN (WGAN) models and presentVehicularGAN(VehiGAN), an ensemble of multiple top-performing WGANs, which transcends the limitations of individual models and improves detection performance. We present a physics-guided data preprocessing technique that generates effective features for ML-based MBDS. In the evaluation, we leverage the state-of-the-art V2X attack simulation tool VASP to create a comprehensive dataset of V2X messages with diverse misbehaviors. Evaluation results show that in 20 out of 35 misbehaviors,VehiGANoutperforms the baseline and exhibits comparable detection performance in other scenarios. Particularly,VehiGANexcels in detecting advanced misbehaviors that manipulate multiple fields in V2X messages simultaneously, replicating unique maneuvers. Moreover,VehiGANprovides approximately 92% improvement in false positive rate under powerful adaptive adversarial attacks, and possesses intrinsic robustness against other adversarial attacks that target the false negative rate. Finally, we make the data and code available for reproducibility and future benchmarking, available athttps://github.com/shahriar0651/VehiGAN. 

Abstract Dissolution trapping is one of the most dominant mechanisms for CO2 storage in subsurface porous media saturated with brine. The CO2 dissolution rate and overall fluid flow dynamics in subsurface formations can vary significantly based on permeability variation. Although some numerical simulations have focused on these factors, detailed flow behavior analysis under nonuniform permeability distribution needs further study. For this purpose, we conduct simulations on the flow behavior of CO2-dissolved brine in two different heterogeneous media. The spatial permeability variations in the cell enable the analysis of complex subsurface storage phenomena, such as changes in finger morphology and preferential dissolution path. Finally, the amount of CO2 dissolved was compared between each case, based on which we draw informed conclusions about CO2 storage sites. The results demonstrated a preferential movement of CO2-dissolved regions toward high permeability regions, whereas a poor sweep efficiency was observed due to minimum dissolution in areas with lower permeability. Furthermore, simulation results also reveal uneven CO2 concentration inside the convective fingers. This study provides fundamental insight into the change in flow behavior at heterogeneous regions, which could be translated into saline aquifer conditions. The proposed workflow in this study could be extended further to analyze complex heterogeneous storage systems at different flow regimes.